Papers by Ashley Gao
AffectCodec: Emotion-Preserving Neural Speech Codec for Expressive Speech Modeling (2026.findings-acl)
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| Challenge: | Existing codecs optimize acoustic reconstruction, leaving emotion expressiveness insufficiently modeled at the representation level. |
| Approach: | They propose an emotion-guided neural speech codec that preserves emotional information while maintaining semantic fidelity and prosodic naturalness. |
| Outcome: | The proposed codec preserves emotional cues while maintaining semantic fidelity and prosodic naturalness. |
Confidence-Aware Ranker Ensembles for Robust In-Context Knowledge Editing (2026.findings-acl)
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| Challenge: | Large language models excel at factual recall, but can propagate stale or incorrect knowledge. |
| Approach: | They propose a feature-weighted ensemble for in-context knowledge editing that calibrates three heterogeneous rankers and extracts simple confidence features from each ranker. |
| Outcome: | The proposed method achieves 88.33% Edit-Success Rate over the best single retriever . it improves edit accuracy without touching model weights and approaches oracle upper bound (91%). |
Role-Guided Annotation and Prototype-Aligned Representation Learning for Historical Literature Sentiment Classification (2025.findings-emnlp)
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| Challenge: | Prior work focused on using sentiment lexicons or leveraging large language models for annotation . lexiconics are often unavailable for historical texts due to limited linguistic resources . |
| Approach: | They propose a role-guided annotation strategy that prompts LLMs to simulate historical perspectives when labeling sentiment. |
| Outcome: | The proposed method outperforms state-of-the-art baselines across historical literature datasets. |